机器学习模型在滑坡敏感性评估中通常被视为黑箱,缺乏对输入特征如何预测结果的分析。这使得很难理解滑坡背后的机制和关键因素。为了增强机器学习模型在广域滑坡敏感性评估中的可解释性,本研究采用Shapely方法探索特征因子从局部、全球,和空间视角。使用随机森林(RF)进行滑坡敏感性评估,支持向量机(SVM),和极限梯度提升(XGBoost)模型,重点是地质复杂的川藏地区。最初,该研究从局部角度揭示了特定关键特征因素对滑坡的贡献。然后研究了特征因子之间相互作用对滑坡发生的总体影响。最后,揭示了各种特征因素对滑坡发生的贡献的空间分布规律。分析表明:(1)XGBoost模型在滑坡敏感性评估中表现突出,实现精度,精度,召回,F1分数,和AUC值分别为0.7815、0.7858、0.7962、0.7910和0.86;(2)Shapely方法确定了四川-西藏地区滑坡的主导因素为海拔(3000-4000m),PGA(1-2g),NDVI(<0.5),和与河流的距离(<3公里);(3)使用Shapely方法,这项研究解释了这些贡献,相互作用机制,滑坡易发性特征因子在局部的空间分布格局,全球,和空间视角。这些发现为深入探索和科学预测滑坡风险提供了新的途径和方法。
Machine learning models are often viewed as black boxes in landslide susceptibility assessment, lacking an analysis of how input features predict outcomes. This makes it challenging to understand the mechanisms and key factors behind
landslides. To enhance the interpretability of machine learning models in wide-area landslide susceptibility assessments, this study uses the Shapely method to explore the contributions of feature factors from local, global, and spatial perspectives. Landslide susceptibility assessments were conducted using random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, focusing on the geologically complex Sichuan-Tibet region. Initially, the study revealed the contributions of specific key feature factors to
landslides from a local perspective. It then examines the overall impact of interactions among feature factors on landslide occurrence globally. Finally, it unveils the spatial distribution patterns of the contributions of various feature factors to landslide occurrence. The analysis indicates the following: (1) The XGBoost model excels in landslide susceptibility assessment, achieving accuracy, precision, recall, F1-score, and AUC values of 0.7815, 0.7858, 0.7962, 0.7910, and 0.86, respectively; (2) The Shapely method identifies the leading factors for
landslides in the Sichuan-Tibet region as Elevation (3000-4000 m), PGA (1-2 g), NDVI (<0.5), and distance to rivers (<3 km); (3) Using the Shapely method, the study explains the contributions, interaction mechanisms, and spatial distribution patterns of landslide susceptibility feature factors across local, global, and spatial perspectives. These findings offer new avenues and methods for the in-depth exploration and scientific prediction of landslide risks.